Image Processing

ABSTRACT

An image recognition approach employs both computer generated and manual image reviews to generate image tags characterizing an image. The computer generated and manual image reviews can be performed sequentially or in parallel. The generated image tags may be provided to a requester in real-time, be used to select an advertisement, and/or be used as the basis of an Internet search. In some embodiments generated image tags are used as a basis for an upgraded image review. A confidence of a computer generated image review may be used to determine whether or not to perform a manual image review.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and benefit of U.S. Provisionalpatent application entitled “Mobile Device Identification Application,”filed May 1, 2013 and given Ser. No. 13/874,815 on filing (pendingcorrection to a provisional application serial number); U.S. Provisionalpatent application entitled “Visual Search,” filed Apr. 4, 2014 andhaving Ser. No. 61/975,691; U.S. Provisional patent application entitled“Visual Search Advertising,” filed Apr. 7, 2014 and having Ser. No.61/976,494; and U.S. Provisional patent application entitled Image“Processing,” filed May 1, 2014 and having Ser. No. 61/987,156. Theabove provisional patent applications are hereby incorporated herein byreference.

BACKGROUND

1. Field of the Invention

The invention is in the field of image processing, and more particularlyin the field of characterizing content of images.

2. Related Art

It is typically more difficult to extract information from images ascompared to text data. However, a significant fraction of information isfound in images. The reliability of automated image recognition systemsis highly dependent on the contents of an image. For example, opticalcharacter recognition is more reliable than facial recognition. It is agoal of image recognition to tag an image. Tagging refers to theidentification of tags (words) that characterize the content of animage. For example an image of a car may be tagged with the words “car,”“Ford Granada,” or “White 1976 Ford Granada with broken headlight.”These tags include varying amounts of information and, as such, may varyin usefulness.

SUMMARY

Embodiments of the invention include a two pronged approach to taggingof images. The first prong is to perform automated image recognition onan image. The automated image recognition results in a review of theimage. The image review includes one or more tags identifying contentsof the image and optionally also a measure of confidence representativeof the reliability of the automated image recognition. The second prongin the approach to tagging of images includes a manual tagging of theimage. Manual tagging includes a person viewing each image, consideringthe content of the image, and manually providing tags representative ofthe content of the image. Automated image recognition has an advantagein that the cost, in time or money, of analyzing each image can berelatively low. Manual tagging of images has an advantage of higheraccuracy and reliability.

Embodiments of the invention combine both automated image recognitionand manual image recognition. In some embodiments automated imagerecognition is performed first. The resulting image review typicallyincludes both one or more tags characterizing the image and a measure ofconfidence in the accuracy of these tags. If the confidence is above apredetermined threshold, then these tags are associated with the imageand provided as an output of the tagging process. If the confidence isbelow the predetermined threshold, then a manual review of the image isperformed. The manual review results in additional and/or different tagsthat characterize the contents of the image. In some embodiments, theautomated image recognition and the manual review of the image areperformed in parallel. The manual review is optionally cancelled oraborted if the automated image recognition results in one or more tagshaving a confidence above the predetermined threshold.

In some embodiments recognition of an image can be upgraded. Upgradingof the image recognition process includes a request for further orimproved tags representative of the content of the image. For example,if automated image recognition results in the tags “white car,” anupgrade of this recognition may result in the tags “white Ford Granada.”In some embodiments, an upgraded review makes use of an expert humanreviewer. For example, the above example may include the use of a humanreviewer with an expert knowledge of automobiles. Other examples ofreviewer expertise are discussed elsewhere herein.

Various embodiments of the invention include features directed towardimproving the accuracy of image recognition while also minimizing cost.By way of example, these features include efficient use of humanreviewers, real-time delivery of image tags, and/or seamless upgrades ofimage recognition. The approaches to image recognition disclosed hereinare optionally used to generate image tags suitable for performinginternet searches and/or selecting advertisements. For example, in someembodiments, image tags are automatically used to perform a Googlesearch and/or sell advertising based on Google's AdWords.

Various embodiments of the invention include an image processing systemcomprising an I/O configured to communicate an image and image tags overa communication network; an automatic identification interfaceconfigured to communicate the image to an automatic identificationsystem and to receive a computer generated review of the image from theautomatic identification system, the computer generated review includingone or more image tags identifying contents of the image; destinationlogic configured to determine a first destination to send the image to,for a first manual review of the image by a first human reviewer; imageposting logic configured to post the image to the destination; reviewlogic configured to receive the a manual review of the image from thedestination and to receive the computer generated review, the manualreview including one or more image tags identifying contents of theimage; response logic configured to provide the image tags of thecomputer generated review and the image tags of the manual review to thecommunication network; memory configured to store the image; and amicroprocessor configured to execute at least the destination logic.

Various embodiments of the invention include a method of processing animage, the method comprising receiving an image from an image source;distributing the image to an automated image identification system;receiving a computer generated review from the automated imageidentification system, the computer generated review including one ormore image tags assigned to the image by the automated imageidentification system and a measure of confidence, the measure ofconfidence being a measure of confidence that the image tags assigned tothe image correctly characterize contents of the image; placing theimage in an image queue; determining a destination; posting the imagefor manual review to a first destination, the first destinationincluding a display device of a human image reviewer; and receiving amanual image review of the image from the destination, the image reviewincluding one or more image tags assigned to the image by the humanimage reviewer, the one or more image tags characterizing contents ofthe image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an image processing system, according to variousembodiments of the invention.

FIG. 2 illustrates an image capture screen, according to variousembodiments of the invention.

FIG. 3 illustrates search results based on an image analysis, accordingto various embodiments of the invention.

FIG. 4 illustrates methods of processing an image, according to variousembodiments of the invention.

FIG. 5 illustrates alternative methods of processing an image, accordingto various embodiments of the invention.

FIG. 6 illustrates methods of managing a reviewer pool, according tovarious embodiments of the invention.

FIG. 7 illustrates methods of receiving image tags in real-time,according to various embodiments of the invention.

FIG. 8 illustrates methods of upgrading an image review, according tovarious embodiments of the invention.

DETAILED DESCRIPTION

FIG. 1 illustrates an Image Processing System 110, according to variousembodiments of the invention. Image Processing System 110 is configuredfor tagging of images and may include one or more distributed computingdevices. For example, Image Processing System 110 may include one ormore servers located at geographically different places. ImageProcessing System 110 is configured to communicate via a Network 115.Network 115 can include a wide variety of communication networks, suchas the internet and/or a cellular telephone system. Network 115 istypically configured to communicate data using standard protocols suchas IP/TCP, FTP, etc. The images processed by Image Processing System 110are received from Image Sources 120 (individually labeled 120A, 120B,etc.). Image Sources 120 can include computing resources connected tothe internet and/or personal mobile computing devices. For example ImageSource 120A may be a web server configured to provide a socialnetworking website or a photo sharing service. Image Source 120B may bea smart phone, camera, or other portable image capture device. Imagesources may be identified by a universal resource locator, an internetprotocol address, a MAC address, a cellular telephone identifier, and/orthe like. In some embodiments Image Processing System 110 is configuredto receive images from a large number of Image Sources 120.

Part of the image tagging performed by Image Processing System 110includes sending images to Destinations 125 (individually labeled 125A,125B, etc.). Destinations 125 are computing devices of human imagereviewers and are typically geographically remote from Image ProcessingSystem 110. Destinations 125 include at least a display and data entrydevices such as a touch screen, keyboard and/or microphone. Destinations125 may include personal computers, computing tablets, smartphones, etc.In some embodiments, Destinations 125 include a (computing) applicationspecifically configured to facilitate review of images. This applicationis optionally provided to Destinations 125 from Image Processing System110. In some embodiments, Image Processing System 110 is configured forhuman image reviewers to log into a user account from Destinations 125.Destinations 125 are typically associated with an individual reviewerand may be identified by an internet protocol address, a MAC address, alogin session identifier, cellular telephone identifier, and/or thelike. In some embodiments, Destinations 125 include an audio to textconverter. Image tagging data provided by a human image reviewer at amember of Destinations 125 is sent to Image Processing System 110. Theimage tagging data can include textual image tags, audio data includingverbalized tags, and/or non-tag information such as upgrade requests orinappropriate (explicit) material designations.

Image Processing System 110 includes an I/O (input/output) 130configured for communicating with external systems. I/O 130 can includerouters, switches, modems, firewalls, and/or the like. I/O 130 isconfigured to receive images from Image Sources 120, to send the imagesto Destinations 125, to receive tagging data from Destinations 125, andoptionally to send image tags to Image Sources 120.

Image Processing System 110 further includes Memory 135. Memory 135includes hardware configured for the non-transient storage of data suchas images, image tags, computing instructions, and other data discussedherein. Memory 135 may include, for example, random access memory (RAM),hard drives, optical storage media, and/or the like. Memory 135 isconfigured to store specific data, as described herein, through the useof specific data structures, indexing, file structures, data accessroutines, security protocols, and/or the like.

Image Processing System 110 further includes at least one Processor 140.Processor 140 is a hardware device such as an electronic microprocessor.Processor 140 is configured to perform specific functions throughhardware, firmware or loading of software instructions into registers ofProcessor 140. Image Processing System 110 optionally includes aplurality of Processor 140. Processor 140 is configured to execute thevarious types of logic discussed herein.

Images received by Image Processing System 110 are first stored in anImage Queue 145. Image Queue 145 is an ordered list of images pendingreview, stored in a sorted list. Images stored in Image Queue 145 aretypically stored in association with image identifiers used to referencethe images and may have different priorities. For example, imagesreceived from a photo sharing website may have lower priority thanimages received from a smartphone. Generally, those images for which arequester is waiting to receive image tags representing an image inreal-time are given higher priority relative to those for which theimage tags are used for some other purpose. Image Queue 145 isoptionally stored in Memory 135.

Within Image Queue 145 images are optionally stored in association withan image identifier or index, and other data associated with each image.For example, an image may be associated with source data relating to oneof Image Sources 120. The source data can include geographic informationsuch as global positioning system coordinates, a street and/or cityname, a zip code, and/or the like. The source data may include aninternet protocol address, a universal resource locator, an accountname, an identifier of a smartphone, and/or the like. Source data canfurther include information about a language used on a member of ImageSources 120, a requested priority, a search request (e.g., an request todo an internet search based on image tags resulting from the image),and/or the like.

In some embodiments, an image within Image Queue 145 is stored inassociation with an indication of a particular subset of the image, thesubset typically including an item of particular interest. For example,a requestor of image tags may be interested in obtaining image tagsrelating to the contents of a particular subset of an image. This canoccur when an image includes several objects. To illustrate, consideringan image of a hand with a ring on one of the fingers, the user may wishto designate the ring as being a particular area of interest. Someembodiments of the invention include an application configured for auser to specify the particular item of interest by clicking on theobject or touching the object on a display of Image Source 120B. Thisspecification typically occurs prior to sending the image to ImageProcessing System 110.

If an image is stored in association with an indication that aparticular subset of the image is of particular importance, then anImage Marking Logic 147 is optionally used to place a mark on the image.The mark being disposed to highlight the particular subset. This markmay be made by modifying pixels of the image corresponding to the subsetand this mark allows a human image reviewer to focus on the markedsubset. For example, the image may be marked with a rectangle or circleprior to the image being posted to one or more of Destinations 125. Inalternative embodiments, Image Marking Logic 147 is included within anapplication configured to execute on one or more of Image Sources 120 orDestinations 125. Image Marking Logic 147 includes hardware, firmware,and/or software stored on a non-transient computer readable medium.

Under the control of Processor 140, images within Image Queue 145 areprovided to an Automatic Identification Interface 150. The images areprovided thus as a function of their priority and position in ImageQueue 145. Automatic Identification interface 150 is configured tocommunicate the image, and optionally any data associated with theimage, to an Automatic Identification System 152. AutomaticIdentification Interface 150 is further configured to receive a computergenerated review of the image from Automatic Identification System 152,the computer generated review including one or more image tagsidentifying contents of the image. In some embodiments, AutomaticIdentification Interface 150 is configured to communicate the image anddata via Network 115 in a format appropriate for an applicationprogramming interface (API) of Automatic Identification System 152. Insome embodiments, Automatic Identification System 152 is included withinImage Processing System 110 and Automatic Identification Interface 150includes, for example, a system call within an operating system or overa local area network.

Automatic Identification System 152 is a computer automated systemconfigured to review images without a need for human input on a perpicture basis. The output of Automatic Identification System 152 is acomputer generated image review (e.g., a review produced without humaninput on a per picture basis.) Rudimentary examples of such systems areknown in the art. See, for example, Kooaba, Clarifai, AlchemyAPI andCatchoom. Automatic Identification System 152 is typically configured toautomatically identify objects within a two dimensional image based onshapes, characters and/or patterns detected within the image. AutomaticIdentification System 152 is optionally configured to perform opticalcharacter recognition and/or barcode interpretation. In someembodiments, Automatic Identification System 152 is distinguished fromsystems of the prior art in that Automatic Identification System 152 isconfigured to provide a computer generated review that is based on theimage subset indication(s) and/or image source data, discussed elsewhereherein.

In addition to one or more image tag(s), a computer generated reviewgenerated by Automatic Identification System 152 optionally includes ameasure of confidence representative of a confidence that the one ormore image tags correctly identify the contents of the image. Forexample, a computer generated review of an image that is primarilycharacters or easily recognizable shapes may have a greater confidencemeasure than a computer generated review of an image that consists ofabstract or ill-defined shapes. Different automated image recognitionsystems may produce different confidence levels for different types ofimages. Automatic Identification Interface 150 and AutomaticIdentification System 152 are optional in embodiments in which automaticidentification is performed by a third party.

Image Processing System 110 further includes a Reviewer Pool 155 andReviewer Logic 157 configured to manage the Reviewer Pool 155. ReviewerPool 155 includes a pool (e.g., group or set) of human image reviewers.Each of the human image reviewers is typically associated with adifferent member of Destinations 125. Memory 135 is optionallyconfigured to store Reviewer Pool 155. In some embodiments, the humanimage reviewers included in Reviewer Pool 155 are classified as “active”and “inactive.” For the purposes of this disclosure, an active humanimage reviewer is considered to be one that is currently providing imagetags or has indicated that they are prepared to provide image tags withminimal delay. In embodiments that include both active and inactivehuman image reviewers, the active reviewers are those that are providedimage for review. The number of active reviewers may be moderated inreal-time in response to a demand for image reviews. For example, theclassification of a human image reviewer may be changed from inactive toactive based on a number of unviewed images in Image Queue 145. Aninactive reviewer is one that is not yet active, that has let the reviewof an image expire, and/or has indicated that they are not available toreview images. Inactive reviewers may request to become activereviewers. Inactive reviewers who have made such a request can bereclassified as active human image reviewers when additional activehuman image reviewers are needed. The determination of which inactivereviewers are reclassified as active reviewers is optionally dependenton a reviewer score (discussed elsewhere herein).

Reviewer Logic 157 is configured to manage Reviewer Pool 155. Thismanagement optionally includes the classification of human imagereviewers as active or inactive. For example, Reviewer Logic 157 may beconfigured to monitor a time that a human image reviewer takes to reviewan image and, if a predetermined maximum review time (referred to hereinas an image expiration time), changing the classification of the humanimage reviewer from active to inactive. In another example, ReviewerLogic 157 may be configured to calculate a review score for a humanimage reviewer. In some embodiments, the review score is indicative ofthe completeness, speed and/or accuracy of image reviews performed bythe particular human image reviewer. The review score can be calculatedor changed based on review times and occasional test images. These testimages may be, for example images placed in Image Queue 145 that havebeen previously reviewed by a different human image reviewer. The reviewscore may also be a function of monetary costs associated with the humanimage reviewer. Reviewer Logic 157 includes hardware, firmware, and/orsoftware stored on a non-transient computer readable medium. In someembodiments, reviewer scores are manually determined by humanmoderators. These human moderators review images and the tags assignedto these images by human image reviewers. Moderators are optionally senta statistical sampling of reviewed images and they assign a score to thetagging of the images. This score is optionally used in determiningreviewer scores.

In some embodiments, Reviewer Logic 157 is configured to monitor statusof human image reviewers in real-time. For example, Reviewer Logic 157may be configured to monitor the entry of individual words or keystrokesas entered by a reviewer at Destination 125A. This monitoring can beused to determine which reviewers are actively reviewing images, whichreviewers have just completed review of an image, and/or which reviewershave not been providing tag input for a number of seconds or minutes.The entry of tag words using an audio device may also be monitored byReviewer Logic 157.

In some embodiments, members of Reviewer Pool 155 are associated with aspecialty in which the human image reviewer has expertise or specialknowledge in. For example, a reviewer may be an expert in automobilesand be associated with that specialty. Other specialties may includeart, plants, animals, electronics, music, food medical specialties,clothing, clothing accessories, collectables, etc. As is discussedelsewhere herein, a specialty of a reviewer may be used to select thatreviewer during an initial manual review and/or during a review upgrade.

The review score and/or specialty associated with a human image reviewerare optionally used by Reviewer Logic 157 to determine which inactivereviewer to make active, when additional active reviewers are required.Reviewer Logic 157 includes hardware, firmware, and/or software storedon a non-transient computer readable medium.

Image Processing System 110 further includes Destination Logic 160.Destination Logic 160 is configured to determine one or moredestinations (e.g., Destinations 125) to send an image to for manualreview. Each of Destinations 125 is associated with a respective humanimage reviewer of Reviewer Pool 155. The determinations made byDestination Logic 160 are optionally based on characteristics of thehuman image reviewer at the determined destination. The destination maybe a computing device, smartphone, tablet computer, personal computer,etc. of the human image reviewer. In some embodiments, the destinationis a browser from which the reviewer has logged into Image ProcessingSystem 110. In some embodiments, determining the destination includesdetermining an MAC address, session identifier, internet protocol and/oruniversal resource locator of one of Destinations 125. Destination Logic160 includes hardware, firmware and/or software stored on anon-transient computer readable medium.

Typically, Destination Logic 160 is configured to determine Destinations125 associated with active rather than inactive human image reviewers asdetermined by Reviewer Logic 157. Destination Logic 160 is alsotypically configured to determine Destinations 125 based on reviewscores of reviewers. For example, those reviewers having higher reviewerscores may be selected for higher priority reviews relative to reviewershaving lower reviewer scores. Thus, the determination of a member ofDestinations 125 can be based on both reviewer scores and image reviewpriority.

In some embodiments, Destination Logic 160 is configured to determineone or more members of Destinations 125 based on the real-timemonitoring of the associated reviewers' input activity. As discussedelsewhere herein, this monitoring may be performed by Reviewer Logic 157and can include detection of individual words or keystrokes entered by ahuman image reviewer. In some embodiments, Destination Logic 160 isconfigured to favor selecting Destination 125A at which a human imagereviewer has just completed a review of an image relative to Destination125B at which a human image reviewer is currently typing image tags on akeyboard.

In some embodiments, Destination Logic 160 is configured to use imagetags received via Automatic Identification System 152 to determine oneor more members of Destinations 125. For example, if an image tag of“car” is received via Automatic Identification Interface 150 thenDestination Logic 160 can use this information to select a member ofDestinations 125 associated with a human image reviewer that has aspecialty in automobiles.

The value of an image review may also be considered in the selection ofa destination for manual review. For example, an image review of highvalue may lead to the determination of a destination associated with ahuman image reviewer having a relatively high review score, while animage review of lower value may lead to the determination of adestination associated with a human image reviewer having a relativelylower review score. In some embodiments, for some image reviews,Destination Logic 160 is configured to select among Destinations 125 soas to minimize a time required to review an image, e.g., to minimize atime until the image tags of the manual review are provided to Network115.

Destination Logic 160 is optionally configured to determine multipledestinations for a single image. For example, a first destination may beselected and then, following an upgrade request, a second destinationmay be determined. The upgrade request may come from the Image Source120A or from a human image reviewer associated with the firstdestination. In some embodiments, Destination Logic 160 is configured todetermine multiple destinations, to which the image will be posted to inparallel. For example, two, three or more destinations, each associatedwith a different human image reviewer, may be determined and the sameimage posted to all determined destinations in parallel. As used in thiscontext, “in parallel” means that the image is posted to at least asecond destination before any part of a review is received from thefirst destination.

In various embodiments, there are a variety of reasons that two or moredestinations may be determined by Destination Logic 160. For example, arequest for an upgraded review may require a human image reviewer havinga particular specialty. Referring to the automotive example, an imagethat is first tagged with the tag “white car” may result in an upgradequest for more information. Destination Logic 160 may be configured tothen select a destination associated with a human image reviewer have aspecialty in automobiles, e.g., a reviewer who can provide the tags“1976 Ford Granada.”

Another instance that may require a second destination occurs when themanual review of an image takes too long. Typically, the tagging of animage should occur within an allotted time period or the review isconsidered to expire. The allotted time period is optionally a functionof the priority of the image review. Those reviews that are intended tooccur in real-time may have a shorter time period relative to lowerpriority reviews. If the review of an image expires, Image ProcessingSystem 110 is optionally configured to provide the image to anadditional human image reviewer associated with a destination determinedby Destination Logic 160.

Another instance that may require a second destination occurs when afirst human reviewer makes an upgrade request. For example, the requestto upgrade the review resulting in a tag of “car” may come from thehuman image reviewer that provided the tag “car.” While this example issimplistic, other examples may include images of more esoteric subjectmatter such as packaged integrated circuits.

Image Processing System 110 further includes Image Posting Logic 165configured to post images for manual review to Destinations 125determined by Destination Logic 160. Posting typically includescommunicating the images to one or more Destinations 125 via Network115. In various embodiments, Image Posting Logic 165 is furtherconfigured to provide information associated with the image toDestinations 125. For example, Image Posting Logic 165 may post, alongwith the image, an indication of a subset of the image (e.g., subsetidentification), an image marked by Image Marking Logic 147, informationidentifying a source of the image (e.g., source data discussed elsewhereherein), a priority of the review of the image, an image expirationperiod, location information associated with the image, and/or the like.As discussed elsewhere herein, source data can includes a universalresource locator, global positioning coordinates, longitude andlatitude, an account identifier, an internet protocol address, a socialaccount, an photo sharing account, and/or the like.

In some embodiments Image Posting Logic 165 is configured to provide animage for manual review to more than one of Destinations 125 at theapproximately the same time. For example, an image may be provided toDestination 125A and Destination 125B in parallel. “Parallel delivery”means, for example, that the image is provided to both Destinations 125Aand 125B before tagging information is received back from either ofthese Destinations 125.

In some embodiments, Image Posting Logic 165 is configured to provide animage for manual review to one or more of Destinations 125 prior toreceiving image tags from Automatic Identification System 152.Alternatively, in some embodiments, Image Posting Logic 165 isconfigured to wait until a computer generated review for the image isreceived from Automatic Identification System 152, prior to posting theimage to one or more of Destinations 125. In these embodiments, thecomputer generated review (including image tags) is optionally alsoposted to the one or more of Destinations 125 in association with theimage.

Image Posting Logic 165 is optionally configured to post identifiers ofimages along with the images. Image Posting Logic 165 includes hardware,firmware and/or software stored on a non-transient computer readablemedium.

Image Processing System 110 further includes Review Logic 170 configuredto manage the manual and automated reviews of images. This managementincludes monitoring progress of reviews, receiving reviews fromAutomatic Identification System 152 and/or Destinations 125. Thereceived reviews include image tags as discussed elsewhere herein. Insome embodiments, Review Logic 170 is configured to control posting ofthe image to one of Destinations 125 based on a measure of confidence.The measure of confidence being representative of a confidence that oneor more image tags already received are correct. These one or more imagetags may be received from Automatic Identification System 152 and/or oneof Destinations 125. For example, in some embodiments if the confidenceof an image review by Automatic Identification System 152 is greaterthan a predetermined threshold, then Review Logic 170 may determine thatmanual review of the image is not necessary. The predetermined thresholdcan be a function of the value of the image review, of the priority ofthe image review, of the number and quality of the availableDestinations 125, and/or the like. Review Logic 170 includes hardware,firmware, and/or software stored on a non-transient computer readablemedium.

In some embodiments, if an image was sent to Automatic IdentificationSystem 152 in parallel with being sent to one or more of Destinations125, then the receipt of a review from Automatic Identification System152 having a confidence above a predetermined threshold may result incancellation of the manual review at the one or more of Destinations 125by Review Logic 170. Likewise, if an image is sent to multipleDestinations 125 in parallel, and an image review is received from afirst of these Destinations 125, then Review Logic 170 is optionallyconfigured to cancel the review requests for the image at the otherDestinations 125. In some embodiments, Review Logic 170 is configured tocancel the review request at the other Destinations 125 once a keystrokeor word is received from the first of the Destinations 125.

In some embodiments Review Logic 170 is configured to monitor activityof a human image reviewer in real-time. This monitoring can includereceiving review inputs from Destinations 125 on a word by word orindividual keystroke basis. As discussed elsewhere herein, the wordsand/or keystrokes are optionally passed on to one of Image Sources 120as they are received by Review Logic 170. The monitoring of a manualreviewer's activity can be used to determine when the review of an imageexpires and/or the progress in completing a manual image review. Thestatus of a human image reviewer may be provided by Review Logic 170 toReviewer Logic 157 in real-time. Using this status, Reviewer Logic 157may change the status of the reviewer from active to inactive, adjust astored review score of the reviewer, establish or change a specialty forthe reviewer, and/or the like.

In some embodiments Review Logic 170 is configured to control posting ofimages to Destinations 125 by receiving measures of confidence (e.g., ofthe accuracy of image reviews) and sending responsive signals toDestination Logic 160 and/or Image Posting Logic 165. As such, ReviewLogic 170 can be configured to control posting of an image to one ormore of Destinations 125 based on a measure of confidence. The measureof confidence being representative of a confidence that one or moreimage tags correctly identify the contents of the image. In someembodiments, Review Logic 170 is configured to receive reviews frommanual image reviewers that include information other than image tags.For example, Review Logic 170 may receive an upgrade request from ahuman image reviewer and cause an upgraded image review to be requested.Review Logic 170 is optionally configured to process other non-taginformation received in a manual or computer generated review. Thisinformation can include identification of the image as being improper(e.g., obscene), identification of the image as containing noidentifiable objects, identification of the image as having been sent toa reviewer of the wrong specialty, and/or the like.

In some embodiments, Review Logic 170 is configured to adjust theconfidence of an image review by comparing image reviews of the sameimage from multiple sources. These image reviews may all be computergenerated, all be manual reviews, or include at least one computergenerated review and at least one manual review.

In some embodiments, Review Logic 170 is configured to provide imagetags received as part of a first (computer generated or manual) reviewand to provide the received image tags to a human image reviewer atDestinations 125B. An agent (e.g., a browser or special purposeapplication) executing on Destination 125B is optionally configured toprovide the image tags of the first review to a display of Destination125B. In this manner, the human image reviewer at Destination 125B canedit (add to, delete and/or replace) the image tags of the first review.For example, image tags received from Destination 125A may be providedto Destination 125B for modification.

In some embodiments, Review Logic 170 is configured to calculate reviewscores based on the results of image reviews received from Destinations125, the time taken for these image reviews, and the accuracy of theseimage reviews.

In some embodiments Review Logic 170 is configured to provide imagereviews to a source of the image, e.g., one of Image Sources 120, usinga Response Logic 175. The image reviews may be provided when the imagereview is complete, on a character by character basis, or on a word byword basis. When provided on a character by character basis or a word byword basis, the image tags are optionally provided to the source of theimage as the characters or words are received from a human imagereviewer. Optionally Response Logic 175 is configured to provide theimage review via Network 115.

Image reviews are not necessarily returned to one of Image Sources 120.For example, if Image Source 120A is a photo sharing service or a socialnetworking website, image reviews of images from Image Source 120A maybe stored in association with an account on the photo sharing service orthe social networking website. This storage can be in Memory 135 or at alocation external to Image Processing System 110, such as at a webserverhosting the website.

In some embodiments, Response Logic 175 is configured to execute asearch based on image tags received in a computer generated and/ormanual image review. The results of this search can be provided to asource of the image, e.g., Image Source 120A or 120B. For example, insome embodiments a user uses a smartphone to create an image with acamera of Image Source 120A. The image is provided to Image Processingsystem 110 which generates an image review of the image using AutomaticIdentification System 152 and Destination 125A. The image reviewincludes image tags that are then automatically used to perform anInternet search (e.g., a google or yahoo search) on the image tags. Theresults of this internet search are then provided to the user'ssmartphone.

In some embodiments, Response Logic 175 is configured to provide imagetags of a computer generated and/or manual review to an AdvertisingSystem 180. Advertising System 180 is configured to selectadvertisements based on the image tags. The selected advertisements areoptionally provided to the source of the image used to generate theimage tags. For example, Response Logic 175 may provide the tags “1976Ford Granada with broken headlight” to Advertising System 180 and, inresponse, Advertising System 180 may select advertisements forreplacement headlights. If the source of the image used to generatethese tags is a website, the advertisements may be displayed on thewebsite. Specifically, if the source of the image is an account on aphoto sharing or social networking website, then the advertisements maybe displayed on that account. Advertising System 180 is optionallyincluded in Image Processing System 110. Advertising System 180 isoptionally configured to take bids for providing advertising in responseto specific tags. Advertising System 180 optionally includes Google'sAdwords.

Image Processing System 110 optionally further includes ContentProcessing Logic 185 configured to extract images for tagging frommembers of Image Sources 120. Content Processing Logic 185 is configuredto parse webpages including images and optionally text, and extractimages from these webpages for tagging. The resulting image tags maythen be provided to Advertising System 180 for selection ofadvertisements that can be placed on the webpage from which the imagewas extracted. In some embodiments, Content Processing Logic 185 isconfigured to emulate browser functions in order to load images thatwould normally be displayed on a webpage. These images may be displayedon a webpage associated with a specific account, a social networkingsite, a photo sharing site, a blogging site, a news site, a dating site,a sports site, and/or the like. Content Processing Logic 185 isoptionally configured to parse metadata tags in order to identifyimages.

FIG. 2 illustrates an Image Capture Screen 210, according to variousembodiments of the invention. Image Capture Screen 210 as illustrated isgenerated by, for example, an application executing on a smartphone orother Image Source 120. Image Capture Screen 210 is includes featuresconfigured to capture an image, mark a specific area of interest, andreceive image tags. Specifically, Image Capture Screen 210 includes aShutter Button 220 configured to take a picture. Once the picture istaken it is optionally automatically sent via Network 115 to ImageProcessing System 110 for tagging. Image Capture Screen 210 optionallyfurther includes a Rectangle 230 configured to highlight a point ofinterest within the image. Rectangle 230 is controllable (e.g., movable)by selecting and/or dragging on the screen using a user input device. Ona typical smartphone this user input device may include a touch screenresponsive to a finger touch. As described elsewhere herein, thepoint/region of interest may be provided to Image Processing System 110in association with an image to be tagged.

Image Capture Screen 210 further includes a Field 240 showing apreviously captured image and resulting image tags. In the example, showthe previously captured image includes the same white cup without theRectangle 230 and the image tags include “White Starbucks Coffee Cup.”Also shown is text stating “Slide for options.”

FIG. 3 illustrates search results based on an image analysis, accordingto various embodiments of the invention. These results are optionallydisplayed automatically or in response to selecting the “Slide foroptions” input shown in FIG. 2. They may be generated by automaticallyexecuting an internet search on the image tags. Illustrated in FIG. 3are a Sponsored Advertisement 310, Related Images 320 and other searchresults 330. The search results are optionally generated usingAdvertising System 180 and image tags generated using Image ProcessingSystem 110. A user may of the option of reviewing previously taggedimages. This history can be stored on Image Source 120A or in Memory135.

FIG. 4 illustrates methods of processing an image, according to variousembodiments of the invention. In these methods an image is received. Theimage is provided to both Automatic Identification System 152 and atleast one of Destinations 125. As a result, both computer generated andmanual image reviews are produced. The methods illustrated in FIG. 4 areoptionally performed using embodiments of the system illustrated inFIG. 1. The method steps illustrated in FIGS. 4-8 may be performed in avariety of alternative orders.

In a Receive Image Step 410 and image is received by Image ProcessingSystem 110. The image is optionally received from one of Image Sources120 via Network 115. The image may be in a standard format such as TIF,JPG, PNG, GIF, etc. The image may be one of a sequence of images thatform an image sequence of a video. The image may have been captured by auser using a camera. The image may have been captured by a user from amovie or television show. In some embodiments Receive Image Step 410includes a user using an image capture application to capture the imageand communicate the image to Image Processing System 110. Thisapplication may be disposed within a camera, television, video displaydevice, multimedia device, and/or the like. Receive Image Step 410 isoptionally facilitate using Content Processing Logic 185.

In an optional Receive Subset Identification Step 415, data identifyingone or more subsets of the image is received by Image Processing System110. Typically, the one or more subsets include a set of image pixels inwhich an item of particular interest is located. The one or more subsetsmay be identified by pixel locations, screen coordinates, areas, and/orpoints on the received image. In some embodiments, the subsets areselected by a user using a touch screen or cursor of one of ImageSources 120.

In an optional Receive Source Data Step 420, source data regarding thesource of the image, received in Receive Image Step 410, is received byImage Processing System 110. As discussed elsewhere herein, the sourcedata can include geographic information, an Internet protocol address, auniversal resource locator, an account name, an identifier of asmartphone, information about a language used on a member of ImageSources 120, a search request, user account information, and/or thelike. In some embodiments, source data is automatically sent by anapplication/agent running on Image Source 120. For example, globalpositioning system coordinates may automatically be generated on asmartphone and provided to Image Processing System 100.

In an optional Receive Analysis Priority Step 425 a priority for thetagging of the image, received in Receive Image Step 410, is receivedwithin Image Processing System 110. In some embodiments, the priority ismanually entered by a user of Image Source 120A. In some embodiments,the priority is dependent on an amount paid for the review of the image.In some embodiments, the priority is dependent on a type of ImageSources 120A. For example, images received from a static website mayautomatically be given a lower priority relative to images received froma handheld mobile device. An image whose source is identified by auniversal resource locator may be given a lower priority relative toimages whose source is identified by a mobile telephone number. As such,the priority is optionally derived from the source data received inReceive Source Data Step 420.

The image and data received in Steps 410-425 are optionally receivedtogether and optionally stored in Memory 135.

In a Distribute Image Step 430, the image, and optionally any associateddata received in Steps 415-425, is distributed to AutomaticIdentification System 152 via Automatic Identification Interface 150.This distribution may be internal to Image Processing System 110 or viaNetwork 115.

In a Receive Automated Response Step 435, a computer generated imagereview is received from Automatic Identification System 152. Thecomputer generated image review includes one or more image tags assignedto the image by Automatic Identification System 152. The computergenerated image review also includes a measure of confidence. Themeasure of confidence is a measure of confidence that the image tagsassigned to the image correctly characterize contents of the image. Forexample, an image including primarily easily recognizable characters mayreceive a higher measure of confidence relative to an image of abstractshapes.

In an Optional Determine Confidence Step 440, the measure of confidenceincluded in the image review is compared with one or more predeterminedlevels. The predetermined levels are optionally a function of thepriority of the image review, a price of the image review, a source ofthe image, and/or the like. In an Optional Confident? Step 445 theprocess proceeds to an optional Perform Search Step 450 if theconfidence of the computer generated image review is above thepredetermined threshold(s) and proceeds to a Queue Image Step 460 if theconfidence of the computer generated image is below the predeterminedthreshold(s). Determine Confidence Step 440 is optionally performedusing Review Logic 170.

In Perform Search Step 450, the image tags assigned to an image are usedto perform a search. For example, the image tag “Ford car” may be usedto automatically perform a google search using the words “Ford” and“car.”

In a Provide Results Step 455, the image tags assigned to the image andoptionally the results of a search performed in Perform Search Step 450are provided to a requester of the image review. For example, if theimage was received from Image Source 120A and Image Source 120A is asmartphone, then the image tags and search results are typicallyprovided to the smartphone. If the image was received from a member ofImage Sources 120, such as a website, that the image tags and optionalsearch results may be provided to a host of the website, to a thirdparty, to Advertising System 180, and/or the like. In some embodiments,the image tags are automatically added to the website such that theimage tags are searchable, e.g., can be searched on to find the reviewedimage.

In Queue Image Step 460, the image is placed in Image Queue 145. Thisplacement optionally includes marking a subset of the image using ImageMarking Logic 147. As described elsewhere herein, the marking istypically configured to identify objects of particular interest in theimage. Advancement of the image in Image Queue 145 may be dependent onthe image's review priority, the source of the image, available humanimage reviewers, the measure of confidence of the computer generatedreview of the image, and/or the like.

In a Determine Destination Step 465 one or more members of Destinations125 are determined for the manual review of the image. The determinationof a destination is optionally based on image tags included in acomputer generated image review received from Automatic IdentificationSystem 152; optionally based on specialties of human image reviewers atdifferent Destinations 120; optionally based on review scores of thesehuman image reviewers, and/or based on other criteria discussed herein.

In a Post Image Step 470, the image is posted to at least one of theDestinations 125 determined in Determine Destination Step 465. In someembodiments, Post Image Step 470 includes posting the image to more thanone of Destinations 125 in parallel. The image is optionally posted viaNetwork 115 and is optionally posted along with a mark highlighting asubset of the image, source data for the image, a time before reviewexpiration for the image, image tags for the image received fromAutomatic Identification System 152, and/or the like.

In a Receive Review Step 475, a manual review of the image is receivedfrom one or more of the determined Destination(s) 125. The manual imagereview may include one or more image tags assigned to the image by ahuman image reviewer. The one or more image tags are representative ofthe content of the image. The manual review may also include an upgraderequest, an indication that the image is unreviewable, an indicationthat the image is improper, an indication that the review expired,and/or the like.

In an Image Tagged? Step 480 the progress of the method is dependent onwhether image tags were received in Receive Review Step 475. If imagetags characterizing the content of the image were received then themethod optionally proceeds with Perform Search Step 450 and ProvideResults Step 455. In these steps the image tags included in the manualimage review and optionally the computer generated image review areused. Use of the image tags in the computer generated image review maybe dependent on the confidence measure of this review.

Steps 460-475 are optional if in Step 445 the confidence measure isfound to be above the predetermined threshold(s).

In an optional Upgrade? Step 485 the progress of the method is dependenton whether an upgrade request has been received. If such a request hasbeen received then the method proceeds to Determine Destination Step 465wherein a second/different member of Destinations 125 is determined. Thedetermination may depend on image tags received in the manual imagereview received in Receive Review Step 475. The upgrade request may bereceived from a human image reviewer or from a requester of the imagereview (from Image Source 120A or 120B, etc.). The upgrade request maybe received after the requestor has had a chance to review the imagetags provided in Provide Results Step 455. For example, the requestormay first receive image tags consisting of “white car” and then requesta review upgrade because they desire further information. The reviewupgrade may result in the image being provided to a human image reviewerwith a specialty in automobiles. This human image review can add to theexisting image tags to produce “white car, 1976 Ford Granada.” In someembodiments, the requester can add source data indicating a subset ofthe image when requesting a review upgrade. For example, the reviewermay wish to indicate particular interest in a broken headlight. Thisserves to direct the human image reviewers attention to this feature ofthe image, produce tags that include “broken headlight,” and result in asearch (Perform Search Step 450), directed toward broken headlights fora 1976 Ford Granada.

In some embodiments, upgrade request are generate automatically byReview Logic 170. For example if an image review appears too brief,e.g., just “car,” then Review Logic 170 may automatically initiate areview upgrade. In some embodiments, the automatic generation of upgraderequests is based on the presence of keywords within a manual imagereview. For example, certain review specialties are associated withlists of keywords. In some embodiments, when one of these keywords arereceived in a manual image review and an automated review upgrade isinitiated. The review upgrade preferably includes a human image reviewerhaving a specialty associated with the received keyword. In a specificexample, one specialty includes “automobiles” and is associated with thekeywords “car,” “truck,” “van,” “convertible,” and “Ford.” When one ofthese keywords is received in a manual image review, Review Logic 170checks with Review Logic 157 to determine if a human image reviewerhaving a specialty in “automobiles” is currently active. If so, then anautomatic upgrade is initiated and the image is sent to the Destination125B of the reviewer having the “automobiles” specialty.

If no upgrade requests are made, then in an End Step 490, the process iscompleted.

FIG. 5 illustrates alternative methods of processing an image, accordingto various embodiments of the invention. In these methods, at least someof Steps 430-445 are performed in parallel with at least some of Steps460-475. The manual image review is in Steps 460-475 may be begun beforethe computer generated review of Steps 430-445 is complete, thus, themanual image review is started before the confidence measure of thecomputer generated review is known. If, in Confident? Step 445, theconfidence measure is found to be above the predetermined threshold(s),then Steps 460-475 are optionally aborted.

Referring to FIG. 6, various embodiments of the invention includemethods of managing of a reviewer pool. The methods including receivingan image for review; selecting (determining) a first member ofDestinations 125 using Destination Logic 160; posting the received imageto the first member of Destinations 125; using Review Logic 170 tomonitor progress of a manual image review of the image at the firstmember of Destinations 125, the monitoring indicating that the reviewhas taken more than a predetermined review time; changing the status ofa human image reviewer associated with the first member of Destinations125 from active to inactive in response to the review taking more thanthe predetermined review time; selecting a second member of Destinations125; posting the image to the second member of Destinations; andreceiving a manual image review from the second member of Destinations125.

Referring to FIG. 7, various embodiments of the invention includemethods of providing real-e feedback of a manual image review. Themethods comprising posting an image to a member of Destinations 125;detecting a first key stroke (or audio) entered by a human imagereviewer at the member of Destinations 125; detecting completion of afirst word of an image tag at the member of Destinations 125; deliveringthe first word to a source of the image; following delivery of the firstword detecting completion of a second word of the image tag at themember of Destinations 125; delivering the second word to the source ofthe image; detection completion of the image tag (e.g., by detecting acarriage return); and associating the image tag with the image.

Referring to FIG. 8, various embodiments of the invention includemethods of managing an upgrade of an image review. The methodscomprising receiving an image; selecting a first member of Destinations125; posting the image to the first member of Destinations 125;receiving a first manual image review from the first member ofDestinations 125; detecting an upgrade request (the upgrade requestbeing received from a source of the image or from the first member ofDestinations 125); selecting a second member of Destinations 125;posting the image to the second member of Destinations 125; optionallyposting the first manual image review to the second member ofDestinations 125 in association with the image; receiving a secondmanual image review from the second member of Destinations 125; anddelivering the second manual image review and optionally the firstmanual image review to a source of the image.

Several embodiments are specifically illustrated and/or describedherein. However, it will be appreciated that modifications andvariations are covered by the above teachings and within the scope ofthe appended claims without departing from the spirit and intended scopethereof. For example, the images discussed herein are optionally part ofa video sequence of a video. Human image reviews may provide image tagsat Destinations 125 using audio input. The audio input can be convertedto text in real-time using audio to text conversion logic disposed onDestinations 125 and/or Image Processing System 110. Image tags areoptionally processed by spellcheck logic. As used herein, the term“Real-time” means without unnecessary delay such that a user can easilywait for completion.

The embodiments discussed herein are illustrative of the presentinvention. As these embodiments of the present invention are describedwith reference to illustrations, various modifications or adaptations ofthe methods and or specific structures described may become apparent tothose skilled in the art. All such modifications, adaptations, orvariations that rely upon the teachings of the present invention, andthrough which these teachings have advanced the art, are considered tobe within the spirit and scope of the present invention. Hence, thesedescriptions and drawings should not be considered in a limiting sense,as it is understood that the present invention is in no way limited toonly the embodiments illustrated.

Computing systems referred to herein, (e.g., Image Processing System110, Images Sources 120 and Destinations 125), can comprise anintegrated circuit, a microprocessor, a personal computer, a server, adistributed computing system, a communication device, a network device,or the like, and various combinations of the same. A computing systemmay also comprise volatile and/or non-volatile memory such as randomaccess memory (RAM), dynamic random access memory (DRAM), static randomaccess memory (SRAM), magnetic media, optical media, nano-media, a harddrive, a compact disk, a digital versatile disc (DVD), and/or otherdevices configured for storing analog or digital information, such as ina database. The various examples of logic noted above can comprisehardware, firmware, or software stored on a computer-readable medium, orcombinations thereof. A computer-readable medium, as used herein,expressly excludes paper. Computer-implemented steps of the methodsnoted herein can comprise a set of instructions stored on acomputer-readable medium that when executed cause the computing systemto perform the steps. A computing system programmed to performparticular functions pursuant to instructions from program software is aspecial purpose computing system for performing those particularfunctions. Data that is manipulated by a special purpose computingsystem while performing those particular functions is at leastelectronically saved in buffers of the computing system, physicallychanging the special purpose computing system from one state to the nextwith each change to the stored data. The logic discussed herein mayinclude hardware, firmware and/or software stored on a computer readablemedium. This logic may be implemented in an electronic device to producea special purpose computing system.

What is claimed is:
 1. An image processing system comprising: an I/Oconfigured to communicate an image and image tags over a communicationnetwork; an automatic identification interface configured to communicatethe image to an automatic identification system and to receive acomputer generated review of the image from the automatic identificationsystem, the computer generated review including one or more image tagsidentifying contents of the image; destination logic configured todetermine a first destination to send the image to, for a first manualreview of the image by a first human reviewer; image posting logicconfigured to post the image to the destination; review logic configuredto receive the a manual review of the image from the destination and toreceive the computer generated review, the manual review including oneor more image tags identifying contents of the image; response logicconfigured to provide the image tags of the computer generated reviewand the image tags of the manual review to the communication network;memory configured to store the image; and a microprocessor configured toexecute at least the destination logic.
 2. The system of claim 1,wherein the automatic identification system is included within the imageprocessing system and is configured to automatically identify objectswithin a two dimensional image based on shapes detected within thetwo-dimensional image.
 3. The system of claim 1, wherein the computergenerated review includes a measure of confidence representative of aconfidence that the one or more image tags of the computer generatedreview correctly identify the contents of the image.
 4. The system ofclaim 1, wherein the destination logic is configured to determine thedestination by detecting use of a keyboard by a human reviewer.
 5. Thesystem of claim 1, wherein the destination logic is configured todetermine the destination based on image tags received from an automaticidentification system.
 6. The system of claim 1, wherein the imageposting logic is further configured to provide an indication of a subsetof the image to the destination.
 7. The system of claim 1, wherein theimage posting logic is further configured to provide informationidentifying a source of the image to the destination, the informationidentifying the source includes a universal resource locator.
 8. Thesystem of claim 1, wherein the image posting logic is further configuredto provide an indication of a source location of the image to thedestination.
 9. The system of claim 1, wherein the image posting logicis configured to provide the image to the destination while the image isbeing reviewed by the automatic identification system.
 10. The system ofclaim 1, wherein the review logic is further configured to controlposting of the image to the destination based on a measure of confidencerepresentative of a confidence that the one or more image tags of thecomputer generated review correctly identify the contents of the image.11. The system of claim 1, wherein the review logic is furtherconfigured to receive a request for further review of the imagefollowing provision of the image tags to the communication network. 12.The system of claim 1, wherein the review logic is further configured toprovide resulting image tags from the first manual review to a seconddestination associated with a second human reviewer.
 13. The system ofclaim 1, wherein the review logic is further configured to provide theone or more image tags to a source of the image, the image tags beingprovided to the source on a word-by-word basis as words are provided bythe human image reviewer.
 14. The system of claim 1, wherein theresponse logic is configured to execute an internet search based on theimage tags of the computer generated review and of the manual review,and to provide results of the internet search to a source of the image.15. The system of claim 1, wherein the response logic is configured toprovide the image tags of the computer generated review and of themanual review to an advertising system, and to receive an identifier ofan advertisement from the advertising system, and to provide theidentifier of the advertisement to a source of the image.
 16. The systemof claim 1, further comprising memory configured to store a reviewerpool of human image reviewers each of the human image reviewers beingassociated with a different destination, wherein at least one of thehuman image reviewers are associated with a review specialty, thereviewer specialty including one of automobiles, art, animals,electronics, medical and clothing.
 17. The system of claim 1, furthercomprising image marking logic configured to receive an image and anindication of an item of interest within the image, and to mark theimage to indicate the item of interest within the image prior to postingthe image to the destination.
 18. The system of claim 1, furthercomprising content processing logic configured to extract the image froman image source, wherein the image source is a photo sharing website ora social networking website.
 19. A method of processing an image, themethod comprising: receiving an image from an image source; distributingthe image to an automated image identification system; receiving acomputer generated review from the automated image identificationsystem, the computer generated review including one or more image tagsassigned to the image by the automated image identification system and ameasure of confidence, the measure of confidence being a measure ofconfidence that the image tags assigned to the image correctlycharacterize contents of the image; placing the image in an image queue;determining a destination; posting the image for manual review to afirst destination, the first destination including a display device of ahuman image reviewer; and receiving a manual image review of the imagefrom the destination, the image review including one or more image tagsassigned to the image by the human image reviewer, the one or more imagetags assigned by the human image reviewer characterizing contents of theimage.
 20. The method of claim 19, further comprising determining thatthe measure of confidence is below a predetermined threshold, theposting the image for manual review being dependent on the determinationthat the measure of confidence is below the predetermined level.
 21. Themethod of claim 19, wherein the manual image review is received word byword or keystroke by keystroke, further comprising providing the manualimage review to the image source in real-time as it is received.
 22. Themethod of claim 19, further comprising receiving an indication of atleast one subset of the received image and marking the received image tohighlight the indicated at least one subset.
 23. The method of claim 19,further comprising receiving source data characterizing a source of theimage, wherein the source data includes global positioning coordinates.24. The method of claim 19, further comprising upgrading review of theimage, the upgrading including selecting a second destination andproviding the image to the second destination.
 25. The method of claim19, further comprising performing an internet search based on the one ormore tags assigned to the image, and reporting results of the search toa source of the image.
 26. The method of claim 19, further comprisingselecting an advertisement based on the one or more image tags assignedby the human image reviewer.